In this paper, we explore this issue for backbone methods in temporal networks. Given a backbone representing only a fraction of the original data, we put forward and explore several systematic procedures to reconstruct surrogate actionable data
by adding auxiliary links to the backbone (see Figure REF )
[1]}, [2]}.
These auxiliary links are extracted at random with a procedure depending on how the backbone was created.
We compare several such procedures applied to backbones obtained through a simple thresholding procedure (serving as baseline) and the significant tie (ST) filter for temporal networks [3]}. We also propose a new version of this filter that considers the data's potential group structure. In each case, we explore how much information about the original data needs to be kept alongside the backbone (e.g., some statistical properties concerning the links that have been filtered out). To show our results' generality, we study temporal-network data with a broad range of topological and temporal structures.
